1. Field of the Invention
The present invention relates to a method for packing and filtering geophysical data in order to retrieve therefrom information on the nature of the subsoil.
2. Description of the Prior Art
Geophysical measurements are conventionally used to provide complementary information, in relation to direct observations in wells, on the geologic properties variations of underground formations. Seismic interpretation is for example based on the analysis of attributes in order to retrieve these geologic properties from the seismic information, in a zone of interest of an underground formation (reservoir zone for example). Another example is log interpretation where the various measurements are simultaneously analyzed to deduce therefrom the petrophysical characteristics of the underground formation studied.
A standard approach for seismic or log attributes analysis involves the use of statistical pattern recognition and estimation methods in order to calibrate the pertinent attributes with the available information obtained in various wellbores.
For example, French Patent 2,768,818 and corresponding U.S. Pat. No. 6,051,651 filed by the assignee describes a method allowing detection of the potential classes in a population of events related to the physical properties of a complex medium such as the subsoil, located from data obtained by subsoil exploration or by in-situ measurements (events of geologic, geophysical nature, events linked with fluid production data, etc.), these events being located by points in a multi-variable space and defined by an attributes vector.
Another example is given by French Patent Application 01/05,675 filed by the assignee, which describes a method for facilitating monitoring over time of the evolution of an underground zone by compared analysis of a certain number n of seismic records successively obtained after n successive 3D seismic surveys (4D seismic method), wherein a pattern recognition technique applied to the whole of the seismic events of several surveys, considered and analyzed simultaneously, is used.
EP Patent A-671,017 and corresponding U.S. Pat. No. 5,638,269 filed by the assignee can also be mentioned, which describe a method allowing establishing of a connection between geologic data obtained by coring or logging in wells and seismic data obtained by means of a seismic exploration survey, which is based on a statistical calibration technique with statistical calibration obtained by bringing together the local geologic data (measured in wells) and seismic attributes read on seismic traces obtained in the immediate vicinity of each well.
The seismic attributes that are subjected to such an interpretation are calculated in the post-stack or in the pre-stack domain.
In the post-stack domain, the conventional attributes are calculated from the amplitudes at the reservoir level, or from the impedance P estimated by means of a stratigraphic inversion.
In the pre-stack domain, the number of pertinent attributes can increase considerably. In this case, the attributes are either the amplitudes at different offsets or incidence angles, or parameters obtained from a pre-stack joint stratigraphic inversion (impedances P and S, density, product of the Lame parameters with the density, etc.).
Concerning the interpretation of logs, the situation is similar to a very large number of measurements being available, hence a large number of attributes allowing characterization of the logs, these attributes being often organized in families of equal physical significance (for example family of the resistivity attributes, family of the attributes associated with the natural or induced radioactivity of the formations, etc.).
A method referred to as principal components analysis (PCA) method, well known in the art, can be applied to these attributes in order to analyze the relations existing between them and to reduce the number of significant attributes. The principal components extracted from the PCA are the new attributes: they define an orthogonal or non-orthogonal (rotation) vector base and are linear combinations of initial variables. They can be used in statistical pattern recognition algorithms and correspond to a multivariate filtering of the initial seismic or log information. Various applications of the PCA method are for example described in:    Dumay, J., Fournier, F., 1988, “Multivariate Statistical Analyses Applied to Seismic Facies Recognition”, Geophysics, 53, 1151-1159,    Hagen, D., C., 1982, “The Application of Principal Component Analysis to Seismic Data Sets”, Geoexpl., 20, 93-111, or    French Patent A-2,772,138 and corresponding U.S. Pat. No. 6,345,108 filed by the assignee.
However, PCA does not take into the attributes group and, consequently, it is often difficult to give a physical interpretation of the principal components, or to relate them clearly to the initial attributes, especially if they are organized in groups of equal physical significance.
The analysis referred to as canonical analysis is also a statistical method well known in the art, which allows establishing the relations that may exist between two sets of variables in order to know if these two sets describe the same properties. This method is for example described by:    Hotelling, H., 1936, “Relations Between Two Sets of Variables”, Biometrika, 28, 321-377.
An example of application of canonical analysis is for example described by:    Fournier, F., and Derain, J. F., 1995, “A Statistical Methodology for Deriving Reservoir Properties from Seismic Data”, Geophysics, 60, 1437-1450.
This method is limited to the study of two sets of variables and does therefore generally not apply to the multicube seismic information or to the multidomain log information. Furthermore, it defines synthetic variables in each subspace associated with the two sets, and not a single vector base allowing packing the whole of the initial variables and to describe the relation between the two sets.
There are several methods allowing generalization of the analysis referred to as canonical analysis by extending the analysis to more than two sets of variables. Different aspects of the prior art in the field considered are described, for example, in the following publications:    Horst, P., 1961, “Relations Among M Sets of Measures”, Psychometrika, 26, No. 2, 129-149,    Caroll, J. D., 1968, “A Generalization of Canonical Correlation Analysis to Three or More Sets of Variables”, Proc. 76th Conv. Amer. Psych. Ass.,    Kettenring, J. R., 1971, “Canonical Analysis of Several Sets of Variables”, 58, 3, 433-450, or    Saporta, G., 1990, “Probabilites, Analyse des Donnees et Statistiques”, Technip, Paris.
These methods however have certain limits. In particular, they do not allow describing each set separately, the synthetic variables cannot be readily related to the various sets and, consequently, be physically interpreted. Furthermore, only the global inertia part they represent can be known, and not the inertia part they represent for each set. It is therefore not possible to filter each set independently.
Another known analysis technique referred to as generalized principal components analysis (GPCA) allows comparison of different sets of variables while describing each one. It is implemented on data of economic nature for example in:    Casin, Ph., 2001, “A Generalization of Principal Component Analysis to K Sets of Variables”, Computational Statistics & Data Analysis, 35, 417-428.